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10 hot data analytics trends — and 5 going cold

Martin Heller | Aug. 11, 2017
Big data, machine learning, data science — the data analytics revolution is evolving rapidly. Keep your BA/BI pros and data scientists ahead of the curve with the latest technologies and strategies for data analysis.

 

Heating up: Jupyter Notebooks

Who: Data scientists

The Jupyter Notebook, originally called IPython Notebook, is an open-source web application that allows data scientists to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

Jupyter Notebooks have become the preferred development environment of many data scientists and ML researchers. They are standard components on Azure, Databricks, and other online services that include machine learning and big data, and you can also run them locally. "Jupyter" is a loose acronym meaning Julia, Python, and R, three of the popular languages for data analysis and the first targets for Notebook kernels, but these days there are Jupyter kernels for about 80 languages.

 

Heating up: Cloud storage and analysis

Who: BI/BA pros, data scientists

One of the mantras of efficient analysis is "do the computing where the data resides." If you don't or can't follow this rule, your analysis is likely to have large delays if the data moves across the local network, and even larger delays if it moves over the Internet. That's why, for example, Microsoft recently added R support to SQL Server.

As the amount of data generated by your company grows exponentially, the capacity of your data centers may not suffice, and you will have to add cloud storage. Once your data is in the cloud, your analysis should be, too. Eventually most new projects will be implemented in the cloud, and existing projects will be migrated to the cloud, moving your company from the CapEx to the OpEx world.

 

Cooling down: Monthly BI reports

Who: BI/BA pros, data scientists

Before self-service business intelligence became popular, BI was the province of IT. Managers described what they thought they wanted to see, business analysts turned that into specifications, and BI specialists created reports to meet the specifications - eventually, given their backlog. Once a report was defined, it was run on a monthly basis essentially forever, and printouts of all possible reports went into management's inboxes on the first of the month, to be glanced at, discussed at meetings, and ultimately either acted on or ignored.

Sometimes the action would be to define a new report to answer a question brought up by an existing report. The whole cycle would start over, and a month or two later the new report would be added to the monthly printout.

Alas, businesses that want to be agile can't respond to environmental and market changes in months: the time between asking a question and getting an answer should be measured in seconds or minutes, not weeks or months.

 

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